Auxiliary function-based integral inequality approach to robust passivity analysis of neural networks with interval time-varying delay

作者：Zhang, Fen; Li, Zhi^*

来源：Neurocomputing, 2018, 306: 189-199.

DOI：10.1016/j.neucom.2018.04.026

• Neural networks
• Parameter uncertainties
• Passivity
• Time-varying delays
• Lyapunov-Krasovskii functional (LKF)

In this paper, we study the problem of passivity for uncertain neural networks with interval time-varying delay. Firstly, a suitable augmented Lyapunov-Krasovskii functional (LKF) containing two triple integral terms is constructed and an auxiliary function-based integral inequality (AFBI) is used to manipulate the augmented single integral terms in the derivative of LKF. Secondly, a special form of the AFBI is applied to deal with the delay-product-type term, which was used to be ignored in the time derivative of a triple integral term. As a result, less conservative delay-dependent passivity criteria are derived for normal delayed neural networks (DNNs) in the form of linear matrix inequalities (LMIs). In addition, with the same LKF, delay-dependent passivity criteria are obtained for normal DNNs without the delay-producttype term. Subsequently, these criteria are extended to DNNs with parameter uncertainties. Finally, four numerical examples and simulations are provided to illustrate the effectiveness of the proposed criteria.

• 出版日期2018-9-6
• 单位西安电子科技大学; 咸阳师范学院